# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import unittest
from functools import partial

import hypothesis.strategies as st
import numpy as np
from auto_scan_test import PassAutoScanTest
from program_config import OpConfig, ProgramConfig, TensorConfig


class TestScaleOneDNNFusePass(PassAutoScanTest):
    def is_program_valid(self, program_config: ProgramConfig) -> bool:
        return True

    def sample_program_config(self, draw):
        data_layout = draw(st.sampled_from(['NCHW', 'NHWC']))
        epsilon = draw(st.floats(min_value=0.0, max_value=0.001))
        fuse_with_relu = draw(st.booleans())
        is_test = draw(st.sampled_from([True]))
        momentum = draw(st.floats(min_value=0.0, max_value=5))
        trainable_statistics = False
        use_global_stats = draw(st.booleans())
        use_onednn1 = draw(st.sampled_from([True]))
        use_cudnn = draw(st.booleans())
        use_onednn2 = draw(st.sampled_from([True]))
        batch_size = draw(st.integers(min_value=1, max_value=4))
        channel = draw(st.integers(min_value=1, max_value=64))
        input_dim1 = draw(st.integers(min_value=1, max_value=512))
        input_dim2 = draw(st.integers(min_value=1, max_value=512))

        def generate_input():
            shape = [input_dim1, input_dim2]
            if data_layout == 'NCHW':
                shape.insert(0, channel)
                shape.insert(0, batch_size)
            else:
                shape.append(channel)
                shape.insert(0, batch_size)
            return np.random.random(shape).astype(np.float32)

        def generate_weight():
            return np.random.random(channel).astype(np.float32)

        batch_norm_op = OpConfig(
            type='batch_norm',
            inputs={
                'X': ['input_data'],
                'Bias': ['Bias'],
                'Mean': ['Mean'],
                'Scale': ['Scale'],
                'Variance': ['Variance'],
            },
            outputs={
                'Y': ['norm_output'],
                'MeanOut': ['Mean'],
                'VarianceOut': ['Variance'],
                'SavedMean': ['SavedMean'],
                'SavedVariance': ['SavedVariance'],
            },
            attrs={
                'data_layout': data_layout,
                'epsilon': epsilon,
                'fuse_with_relu': fuse_with_relu,
                'is_test': is_test,
                'momentum': momentum,
                'trainable_statistics': trainable_statistics,
                'use_global_stats': use_global_stats,
                'use_onednn': use_onednn1,
            },
        )

        relu_op = OpConfig(
            type='relu',
            inputs={'X': ['norm_output']},
            outputs={'Out': ['relu_output']},
            attrs={'use_cudnn': use_cudnn, 'use_onednn': use_onednn2},
        )

        model_net = [batch_norm_op, relu_op]

        program_config = ProgramConfig(
            ops=model_net,
            weights={
                'Bias': TensorConfig(data_gen=partial(generate_weight)),
                'Mean': TensorConfig(data_gen=partial(generate_weight)),
                'Scale': TensorConfig(data_gen=partial(generate_weight)),
                'Variance': TensorConfig(data_gen=partial(generate_weight)),
            },
            inputs={
                'input_data': TensorConfig(data_gen=partial(generate_input))
            },
            outputs=['relu_output'],
        )

        return program_config

    def sample_predictor_configs(self, program_config):
        config = self.create_inference_config(use_onednn=True)
        yield config, ['batch_norm'], (1e-5, 1e-5)

    def test(self):
        self.run_and_statistics(
            quant=False, passes=['batch_norm_act_fuse_pass']
        )


if __name__ == '__main__':
    unittest.main()
